Protein structure information prediction method and apparatus, device, and storage medium

A device for predicting structure information of a protein, comprising a memory for storing computer instructions and a processor in communication with the memory, wherein, when the processor executes the computer instructions, the processor is configured to cause the device to:

• perform sequence alignment query in a first database according to an amino acid sequence of the protein to obtain multi-sequence aligned data;
• perform feature extraction on the multi-sequence aligned data to obtain an initial sequence feature;
• process the initial sequence feature by using a sequence feature augmentation model to obtain an augmented sequence feature of the protein, the sequence feature augmentation model being a machine learning model trained by using a sample initial sequence feature and a sample augmented sequence feature, the sample initial sequence feature being obtained by performing sequence alignment query in the first database according to a sample amino acid sequence, the sample augmented sequence feature being obtained by performing sequence alignment query in a second database according to the sample amino acid sequence, and a data scale of the second database being greater than a data scale of the first database;
• and
• predict structure information of the protein based on the augmented sequence feature.

A non-transitory storage medium for storing computer readable instructions, the computer readable instructions, when executed by a processor in a device, causing the processor to:

• perform sequence alignment query in a first database according to an amino acid sequence of a protein to obtain multi-sequence aligned data;
• perform feature extraction on the multi-sequence aligned data to obtain an initial sequence feature;
• process the initial sequence feature by using a sequence feature augmentation model to obtain an augmented sequence feature of the protein, the sequence feature augmentation model being a machine learning model trained by using a sample initial sequence feature and a sample augmented sequence feature, the sample initial sequence feature being obtained by performing sequence alignment query in the first database according to a sample amino acid sequence, the sample augmented sequence feature being obtained by performing sequence alignment query in a second database according to the sample amino acid sequence, and a data scale of the second database being greater than a data scale of the first database;
• and
• predict structure information of the protein based on the augmented sequence feature.